Linguistic modeling of complex irregular systems is helpful for the generation of decision making controls. In the various existing Fuzzy models, proposed by Mamdani, Sugeno, and Tsukamoto, the concepts of the set of membership functions and different Fuzzy logic rules to reason about data were addressed. The time control issues were not discussed in these models. In this paper, a new model is ...

In this paper, a new method of predictive control is presented. In this approach, a well-known method of predictive functional control is combined with fuzzy model of the process. The prediction is based on fuzzy model given in the form of Takagi–Sugeno (T–S) type. The proposed fuzzy predictive control has been evaluated by implementation on heat-exchanger plant, which exhibits a strong nonline...

In recent years, the Takagi–Sugeno (T–S) fuzzy model has been commonly used for the approximation of nonlinear systems. Using the T-S fuzzy model, nonlinear systems can be converted into linear time-varying systems, which can reduce approximation errors compared with the conventional Taylor approximation. In this paper, we propose a new nonlinear filter with a finite impulse response (FIR) stru...

An intelligent robot needs to be able to understand human emotions, and to understand and generate actions through cognitive systems that operate in a similar way to human cognition. In this chapter, we mainly focus on developing an online incremental learning system of emotions using Takagi-Sugeno (TS) fuzzy model. Additionally, we present a general overview for understanding and generating mu...

This paper proposes a Takagi-Sugeno neuro-fuzzy inference system for direct torque and stator reactive power control applied to a doubly fed induction motor. The control variables (d-axis and q-axis rotor voltages) are determined through a control system composed by a neuro-fuzzy inference system and a first order Takagi-Sugeno fuzzy logic controller. Experimental results are presented to valid...

The paper presents development and tuning tehniques and solutions for PI and PID controllers, and Takagi-Sugeno fuzzy controllers with PI and PID type dynamics meant for applications which can be characterised with low order benchmark type modeles (for example electrical and hydraulic driving and positioning systems).Two type of plants and two control structures with homogenous and with non-hom...

Fuzzy C-means Clustering algorithm (FCM) is a method that is frequently used in pattern recognition. It has the advantage of giving good modeling results in many cases, although, it is not capable of specifying the number of clusters by itself. In FCM algorithm most researchers fix weighting exponent (m) to a conventional value of 2 which might not be the appropriate for all applications. Conse...

The poor scaling behavior of grid-partitioning fuzzy systems in case of increasing data dimensionality suggests using fuzzy systems with a scatter-partition of the input space. Jang has shown that zero-order Sugeno fuzzy systems are equivalent to radial basis function networks (RBFNs). Methods for nding scatter partitions for RBFNs are available, and it is possible to use them for creating scat...

When controlling Takagi-Sugeno fuzzy systems, verification of some sector conditions is usually assumed. However, setpoint changes may alter the sector bounds. Alternatively, setpoint changes may be considered as an offset addition in many cases, and hence affine Takagi-Sugeno models may be better suited to this problem. This work discusses a nonconstant change of variable in order to carry out...

This is the first paper in a series of two papers describing a novel generalization of classical hidden Markov models using fuzzy measures and fuzzy integrals. In this paper, we present the theoretical framework for the generalization and, in the second paper, we describe an application of the generalized hidden Markov models to handwritten word recognition. The main characteristic of the gener...